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Stratification - Stirling 20071 Concepts and Measures: Empirical Evidence on the interpretation of ESeC and other occupation-based social classifications.

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Presentation on theme: "Stratification - Stirling 20071 Concepts and Measures: Empirical Evidence on the interpretation of ESeC and other occupation-based social classifications."— Presentation transcript:

1 Stratification - Stirling Concepts and Measures: Empirical Evidence on the interpretation of ESeC and other occupation-based social classifications Paul Lambert University of Stirling Erik Bihagen University of Stockholm Paper presented to Social Stratification Research Seminar, Stirling 5-7 September 2007

2 Stratification - Stirling Summary: occupation-based social classifications Relevance of reviewing lots of schemes (1) Broad concordance of most measures (2) Optimum measures are ambiguous (1) Lots of overlap in conceptual correlates (3) A small residual difference does reflect concepts Sensible taxonomies can rarely be judged true or false, only more or less useful for a given purpose [Mills & Evans, 2003:80] [EGP]...has a clear theoretical basis, therefore differences between groups in health outcomes can be attributed to the specific employment relations that characterise each group [Shaw et al., 2007:78]

3 Stratification - Stirling This review Relationships between concepts and measures Properties of various contemporary occupation-based social classifications –via SOC90 / NYK/ ISCO88 and employment status ESeC [Rose and Harrison 2007] –European Socio-Economic Classification High degree of replicability Empirical validation / criterion validity Standardisation / consistency / widespread use Theoretical integration (with EGP) Compare with unemployment [Elias & McKnight 2003; Chan & Goldthorpe 2007; Schizzerotto et al 2007]

4 Stratification - Stirling Class 1: Large employers, higher grade professional, administrative and managerial occupations: 'the higher salariat'Class 1 Class 2: Lower grade professional, administrative and managerial occupations: higher grade technician and supervisory occupations: 'the lower salariat'Class 2 Class 3: Intermediate occupations: 'higher grade white collar workers'Class 3 Classes 4 and 5: Small employers and self-employed in non- professional occupations: 'petit-bourgeoisie or independents'Classes 4 and 5 Class 6: Lower supervisory and lower technician occupations: 'higher grade blue collar workers'Class 6 Class 7: Lower services, sales and clerical occupations: 'lower grade white collar workers'Class 7 Class 8: Lower technical occupations: 'skilled workers'Class 8 Class 9: Routine occupations: 'semi- and unskilled workers'Class 9 Class 10: Never worked and long-term unemployed: 'unemployed'Class 10 The non-employed Six, five and three class models

5 Stratification - Stirling Micro-data Britain BHPS 1991, 4537 adults yrs in work 2710 adults observed every year till 2002 Sweden LNU 1991, 2538 adults yrs in work Linked to PRESO administrative data until 2002 [Tomas Korpi] Unemployment (m/f; employees)BrSw Ever Unemployed % / 23%36% / 39% Unemployed for >1 year % / 6%26% / 29% Incidence rate (time Un. / active time)3.4 / 2.3 Cumulative rate (log of total time Un.)1.5 / / 2.3

6 Stratification - Stirling Reviewing occupation-based social classifications? GEODE – Grid Enabled Occupational Data Environment, [e.g. Lambert et al 2007, International Journal of Digital Curation]

7 Stratification - Stirling 20077

8 8 => 31 Occupation-based social classifications ES5 Employment Status (5)WR Wright (12 categories) ES2 Employment Status (2)WR9 Wright (9)CM CAMSIS (male scale) E9 ESeC (9 categories)G11 EGP (11 categories)CF CAMSIS (female scale) E6 ESeC (6 categories)G7 EGP (7 categories)CM2 CAMSIS (male scale, S) E5 ESeC (5 categories)G5 EGP (5 categories)CF2 CAMSIS (female, S) E3 ESeC (3 categories)G3 EGP (3 categories)CG Chan-Goldthorpe status E2 ESeC (2 categories)G2 EGP (2 categories)AWM Wage mobility score K4 Skill (4 ISCO categories)MN Manual / Non-M (2)WG1 Wage score (S) O17 Oesch work logic (17)WG2 Wage score (S) O8 Oesch work logic (8)ISEI (via ISCO88)WG3 Wage score (B) O4 Oesch work logic (4)SIOPS (via ISCO88)GN Gender segregation index

9 Stratification - Stirling Results: Concepts and measures 1)Broad concordance of schemes 2)Ambiguity of optimal schemes 3)Some residual differences do reflect conceptual origins

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13 Stratification - Stirling Results: Concepts and measures 1)Broad concordance of schemes Measures mostly measure the same thing Generalised concepts are better Criterion validity is asymmetric [cf. Tahlin 2007] 2)Ambiguity of optimal schemes 3)Some residual differences do reflect conceptual origins

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15 Stratification - Stirling Results: Concepts and measures 1)Broad concordance of schemes 2)Ambiguity of optimal schemes Balancing explanatory power and parsimony No schemes stand out as substantially stronger ESeC & EGP 3- and 2-class versions limited AWM favourable in Sweden 3)Some residual differences do reflect conceptual origins

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17 Stratification - Stirling EGP cf. CAMSIS – critical individuals Britain (males) Better EGP predicted risk of Un. (H – rightly higher; L – rightly lower) 7121 (L) Builders (traditional) 8322 (L) Car / taxi drivers 1314 (L) Wholesale / retail managers 7141 (L) Painters 7231 (H) Motor mechanics 2411 (H) Accountants 4131 (H) Stock clerks 7124 (H) Carpenters / joiners 8324 (H) Truck / Lorry drivers Better CAMSIS predicted risk of Un. (H – rightly higher; L – rightly lower) 5169 (L) Protective service workers 4212 (L) Tellers / counter clerks 4190 (L) Office clerks 7230 (L) Machinery mechanics/fitters 1314 (H) Wholesale / retail managers

18 Stratification - Stirling Results: Concepts and measures 1)Broad concordance of schemes 2)Ambiguity of optimal schemes 3)Some residual differences do seem to reflect conceptual origins Differences between schemes diminish but dont vanish G11 in Br explains more Unemp. [as Chan & Goldthorpe 2007] E9 in Sweden explains more Unemp. ??Are empirical differences due to (the concepts / employment relations of) certain specific occ.s

19 Stratification - Stirling Conclusions Do measures measure concepts? –Yes (sometimes) – criterion validity –No (not uniquely) How should we choose between measures? –Practical issues –Conceptual assumptions – generalised schemes What about ESeC? –Few clear strengths in empirical properties –Practical advantages if widely used

20 Stratification - Stirling References Chan, T. W., & Goldthorpe, J. H. (2007). Class and Status: The Conceptual Distinction and its Empirical Relevance. American Sociological Review, 72, Elias, P., & McKnight, A. (2003). Earnings, Unemployment and the NS-SEC. In D. Rose & D. J. Pevalin (Eds.), A Researcher's Guide to the National Statistics Socio-Economic Classification. London: Sage. Goldthorpe, J. H., & McKnight, A. (2006). The Economic Basis of Social Class. In S. L. Morgan, D. B. Grusky & G. S. Fields (Eds.), Mobility and Inequality. Stanford: Stanford University Press. Lambert, P. S., Tan, K. L. L., Turner, K. J., Gayle, V., Prandy, K., & Sinnott, R. O. (2007). Data Curation Standards and Social Science Occupational Information Resources. International Journal of Digital Curation, 2(1), Mills, C., & Evans, G. (2003). Employment Relations, Employment Conditions and the NS-SEC. In D. Rose & D. J. Pevalin (Eds.), A Researchers Guide to the National Statistics Socio-economic Classification (pp ). London: Sage. Rose, D., & Harrison, E. (2007). The European Socio-economic Classification: A New Social Class Scheme for Comparative European Research. European Societies, 9(3), Schizzerotto, A., Barone, R., & Arosio, L. (2006). Unemployment risks in four European countries: an attempt of testing the construct validity of the ESeC scheme. Bled, Slovenia, and Paper presented to the Workshop on the Application of ESeC within the European Union and Candidate Countries, June Shaw, M., Galobardes, B., Lawlor, D. A., Lynch, J., Wheeler, B., & Davey Smith, G. (2007). The Handbook of Inequality and Socioeconomic Position: Concepts and Measures. Bristol: Policy Press. Tahlin, M. (forthcoming). Class Clues. European Sociological Review.

21 Stratification - Stirling Appendices

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23 Stratification - Stirling Background – handling occupational data [e.g. Lambert et al 2007, International Journal of Digital Curation] Model is: 1)Record and preserve source occupational data (i.e OUG) 2)Use a transparent translation code to derive occupation-based social classifications Challenges include: –Locating occupational information resources –Large volumes of data (country; time; updates) –Detail on occupational index units (OUGs) –Gaps in working practices (software; NSIs vs academics) ESeC has many attractive features: well documented scheme with criterion validity; transparent access in SPSS; wide adoption likely

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